Using neural population decoding to understand high level visual processing

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Brain and Cognitive Sciences, 2011.

Bibliographic Details
Main Author: Meyers, Ethan M
Other Authors: Tomaso Poggio.
Format: Thesis
Language:eng
Published: Massachusetts Institute of Technology 2011
Subjects:
Online Access:http://hdl.handle.net/1721.1/62718
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author Meyers, Ethan M
author2 Tomaso Poggio.
author_facet Tomaso Poggio.
Meyers, Ethan M
author_sort Meyers, Ethan M
collection MIT
description Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Brain and Cognitive Sciences, 2011.
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spelling mit-1721.1/627182019-04-12T15:19:17Z Using neural population decoding to understand high level visual processing Meyers, Ethan M Tomaso Poggio. Massachusetts Institute of Technology. Dept. of Brain and Cognitive Sciences. Massachusetts Institute of Technology. Dept. of Brain and Cognitive Sciences. Brain and Cognitive Sciences. Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Brain and Cognitive Sciences, 2011. Cataloged from PDF version of thesis. Includes bibliographical references. The field of neuroscience has the potential to address profound questions including explaining how neural activity enables complex behaviors and conscious experience. However, currently the field is a long way from understanding these issues, and progress has been slow. One of the main problems holding back the pace of discovery is that it is still unclear how to interpret neural activity once it has been recorded. This lack of understanding has led to many different data analysis methods, which makes it difficult to evaluate the validity and importance of many reported results. If a clearer understanding of how to interpret neural data existed, it should be much easier to answer other questions about how the brain functions. In this thesis I describe how to use a data analysis method called 'neural population decoding' to analyze data in a way that is potentially more relevant for understanding neural information processing. By applying this method in novel ways to data from several vision experiments, I am able to make several new discoveries, including the fact that abstract category information is coded in the inferior temporal cortex (ITC) and prefrontal cortex (PFC) by dynamic patterns of neural activity, and that when a monkey attends to an object in a cluttered display, the pattern of ITC activity returns to a state that is similar to when the attended object is presented alone. These findings are not only interesting for insights that they give into the content and coding of information in high level visual areas, but they also demonstrate the benefits of using neural population decoding to analyze data. Thus, the methods developed in this thesis should enable more rapid progress toward an algorithmic level understanding of vision and information processing in other neural systems. by Ethan M. Meyers. Ph.D. 2011-05-09T15:24:49Z 2011-05-09T15:24:49Z 2011 2011 Thesis http://hdl.handle.net/1721.1/62718 715390686 eng M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission. http://dspace.mit.edu/handle/1721.1/7582 259 p. application/pdf Massachusetts Institute of Technology
spellingShingle Brain and Cognitive Sciences.
Meyers, Ethan M
Using neural population decoding to understand high level visual processing
title Using neural population decoding to understand high level visual processing
title_full Using neural population decoding to understand high level visual processing
title_fullStr Using neural population decoding to understand high level visual processing
title_full_unstemmed Using neural population decoding to understand high level visual processing
title_short Using neural population decoding to understand high level visual processing
title_sort using neural population decoding to understand high level visual processing
topic Brain and Cognitive Sciences.
url http://hdl.handle.net/1721.1/62718
work_keys_str_mv AT meyersethanm usingneuralpopulationdecodingtounderstandhighlevelvisualprocessing